Automatic Removal of Mechanical Fixations from CT Imagery with Particle Swarm Optimisation

Mohammad Hashem Ryalat; Stephen Laycock; Mark Fisher

doi:10.1007/978-3-319-56148-6_37

Automatic Removal of Mechanical Fixations from CT Imagery with Particle Swarm Optimisation

Mohammad Hashem Ryalat, Stephen Laycock, Mark Fisher

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

26 Downloads (Pure)

Abstract

Fixation devices are used in radiotherapy treatment of head and neck cancers to ensure successive treatment fractions are accurately targeted. Typical fixations usually take the form of a custom made mask that is clamped to the treatment couch and these are evident in many CT data sets as radiotherapy treatment is normally planned with the mask in place. But the fixations can make planning more difficult for certain tumor sites and are often unwanted by third parties wishing to reuse the data. Manually editing the CT images to remove the fixations is time consuming and error prone. This paper presents a fast and automatic approach that removes artifacts due to fixations in CT images without
affecting pixel values representing tissue. The algorithm uses particle swarm optimisation to speed up the execution time and presents results from five CT data sets that show it achieves an average specificity of 92.01% and sensitivity of 99.39%.

Original language	English
Title of host publication	Bioinformatics and Biomedical Engineering
Subtitle of host publication	5th International Work-Conference, IWBBIO 2017, Granada, Spain, April 26–28, 2017, Proceedings, Part I
Editors	Ignacio Rojas, Francisco Ortuño
Publisher	Springer
Pages	419-431
Number of pages	13
ISBN (Electronic)	978-3-319-56148-6
ISBN (Print)	978-3-319-56147-9
DOIs	https://doi.org/10.1007/978-3-319-56148-6_37
Publication status	Published - 2017
Event	5th International Work-Conference: IWBBIO 2017 - Granada, Spain Duration: 26 Apr 2017 → 28 Apr 2017

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	10208
ISSN (Print)	0302-9743

Conference

Conference	5th International Work-Conference
Country/Territory	Spain
City	Granada
Period	26/04/17 → 28/04/17

Keywords

Immobilization Mask
CT Images
Head and Neck Cancer

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-319-56148-6_37

Accepted manuscriptAccepted author manuscript, 1.19 MB

Stephen Laycock
- S.Laycockuea.acuk
- School of Computing Sciences - Professor of Computer Graphics
- Interactive Graphics and Audio - Member
Person: Research Group Member, Academic, Teaching & Research

Cite this

Ryalat, M. H., Laycock, S., & Fisher, M. (2017). Automatic Removal of Mechanical Fixations from CT Imagery with Particle Swarm Optimisation. In I. Rojas, & F. Ortuño (Eds.), Bioinformatics and Biomedical Engineering: 5th International Work-Conference, IWBBIO 2017, Granada, Spain, April 26–28, 2017, Proceedings, Part I (pp. 419-431). (Lecture Notes in Computer Science; Vol. 10208). Springer. https://doi.org/10.1007/978-3-319-56148-6_37

Ryalat, Mohammad Hashem ; Laycock, Stephen ; Fisher, Mark. / Automatic Removal of Mechanical Fixations from CT Imagery with Particle Swarm Optimisation. Bioinformatics and Biomedical Engineering: 5th International Work-Conference, IWBBIO 2017, Granada, Spain, April 26–28, 2017, Proceedings, Part I. editor / Ignacio Rojas ; Francisco Ortuño. Springer, 2017. pp. 419-431 (Lecture Notes in Computer Science).

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title = "Automatic Removal of Mechanical Fixations from CT Imagery with Particle Swarm Optimisation",

abstract = "Fixation devices are used in radiotherapy treatment of head and neck cancers to ensure successive treatment fractions are accurately targeted. Typical fixations usually take the form of a custom made mask that is clamped to the treatment couch and these are evident in many CT data sets as radiotherapy treatment is normally planned with the mask in place. But the fixations can make planning more difficult for certain tumor sites and are often unwanted by third parties wishing to reuse the data. Manually editing the CT images to remove the fixations is time consuming and error prone. This paper presents a fast and automatic approach that removes artifacts due to fixations in CT images withoutaffecting pixel values representing tissue. The algorithm uses particle swarm optimisation to speed up the execution time and presents results from five CT data sets that show it achieves an average specificity of 92.01% and sensitivity of 99.39%.",

keywords = "Immobilization Mask, CT Images, Head and Neck Cancer",

author = "Ryalat, {Mohammad Hashem} and Stephen Laycock and Mark Fisher",

year = "2017",

doi = "10.1007/978-3-319-56148-6_37",

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isbn = "978-3-319-56147-9",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "419--431",

editor = "Rojas, { Ignacio} and Francisco Ortu{\~n}o",

booktitle = "Bioinformatics and Biomedical Engineering",

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note = "5th International Work-Conference : IWBBIO 2017 ; Conference date: 26-04-2017 Through 28-04-2017",

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Ryalat, MH, Laycock, S & Fisher, M 2017, Automatic Removal of Mechanical Fixations from CT Imagery with Particle Swarm Optimisation. in I Rojas & F Ortuño (eds), Bioinformatics and Biomedical Engineering: 5th International Work-Conference, IWBBIO 2017, Granada, Spain, April 26–28, 2017, Proceedings, Part I. Lecture Notes in Computer Science, vol. 10208, Springer, pp. 419-431, 5th International Work-Conference, Granada, Spain, 26/04/17. https://doi.org/10.1007/978-3-319-56148-6_37

Automatic Removal of Mechanical Fixations from CT Imagery with Particle Swarm Optimisation. / Ryalat, Mohammad Hashem; Laycock, Stephen ; Fisher, Mark.

Bioinformatics and Biomedical Engineering: 5th International Work-Conference, IWBBIO 2017, Granada, Spain, April 26–28, 2017, Proceedings, Part I. ed. / Ignacio Rojas; Francisco Ortuño. Springer, 2017. p. 419-431 (Lecture Notes in Computer Science; Vol. 10208).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Automatic Removal of Mechanical Fixations from CT Imagery with Particle Swarm Optimisation

AU - Ryalat, Mohammad Hashem

AU - Laycock, Stephen

AU - Fisher, Mark

PY - 2017

Y1 - 2017

N2 - Fixation devices are used in radiotherapy treatment of head and neck cancers to ensure successive treatment fractions are accurately targeted. Typical fixations usually take the form of a custom made mask that is clamped to the treatment couch and these are evident in many CT data sets as radiotherapy treatment is normally planned with the mask in place. But the fixations can make planning more difficult for certain tumor sites and are often unwanted by third parties wishing to reuse the data. Manually editing the CT images to remove the fixations is time consuming and error prone. This paper presents a fast and automatic approach that removes artifacts due to fixations in CT images withoutaffecting pixel values representing tissue. The algorithm uses particle swarm optimisation to speed up the execution time and presents results from five CT data sets that show it achieves an average specificity of 92.01% and sensitivity of 99.39%.

AB - Fixation devices are used in radiotherapy treatment of head and neck cancers to ensure successive treatment fractions are accurately targeted. Typical fixations usually take the form of a custom made mask that is clamped to the treatment couch and these are evident in many CT data sets as radiotherapy treatment is normally planned with the mask in place. But the fixations can make planning more difficult for certain tumor sites and are often unwanted by third parties wishing to reuse the data. Manually editing the CT images to remove the fixations is time consuming and error prone. This paper presents a fast and automatic approach that removes artifacts due to fixations in CT images withoutaffecting pixel values representing tissue. The algorithm uses particle swarm optimisation to speed up the execution time and presents results from five CT data sets that show it achieves an average specificity of 92.01% and sensitivity of 99.39%.

KW - Immobilization Mask

KW - CT Images

KW - Head and Neck Cancer

U2 - 10.1007/978-3-319-56148-6_37

DO - 10.1007/978-3-319-56148-6_37

M3 - Conference contribution

SN - 978-3-319-56147-9

T3 - Lecture Notes in Computer Science

SP - 419

EP - 431

BT - Bioinformatics and Biomedical Engineering

A2 - Rojas, Ignacio

A2 - Ortuño, Francisco

PB - Springer

T2 - 5th International Work-Conference

Y2 - 26 April 2017 through 28 April 2017

ER -

Ryalat MH, Laycock S , Fisher M. Automatic Removal of Mechanical Fixations from CT Imagery with Particle Swarm Optimisation. In Rojas I, Ortuño F, editors, Bioinformatics and Biomedical Engineering: 5th International Work-Conference, IWBBIO 2017, Granada, Spain, April 26–28, 2017, Proceedings, Part I. Springer. 2017. p. 419-431. (Lecture Notes in Computer Science). Epub 2017 Apr 1. doi: 10.1007/978-3-319-56148-6_37

Automatic Removal of Mechanical Fixations from CT Imagery with Particle Swarm Optimisation

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

Profiles

Stephen Laycock

Cite this